Continual Learning Should Move Beyond Incremental Classification

2025-02-17

AI Summary

Current continual learning (CL) research overemphasizes incremental classification, leading to theoretical limitations and poor practical applicability. This stems from neglecting three core challenges: the intrinsic continuity of learning problems, the appropriate choice of similarity metric space, and the design of learning objectives beyond classification. To address these, we propose the first general-purpose CL framework for dynamic environments: it models temporal evolution via distributional processes, formalizes a theory of continuous task spaces, and integrates density estimation with generative learning objectives. Departing from the classification-centric paradigm, our framework significantly enhances robustness and generalization in real-world scenarios—including multi-object classification, robot control, continuous domain adaptation, and high-level conceptual memory—thereby establishing a more rigorous theoretical foundation and expanding the practical scope of continual learning.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Intelligent Robots: Learning & Optimization for ROB
• Multiagent Systems: Multiagent Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Continual learning (CL) is the sub-field of machine learning concerned with accumulating knowledge in dynamic environments. So far, CL research has mainly focused on incremental classification tasks, where models learn to classify new categories while retaining knowledge of previously learned ones. Here, we argue that maintaining such a focus limits both theoretical development and practical applicability of CL methods. Through a detailed analysis of concrete examples - including multi-target classification, robotics with constrained output spaces, learning in continuous task domains, and higher-level concept memorization - we demonstrate how current CL approaches often fail when applied beyond standard classification. We identify three fundamental challenges: (C1) the nature of continuity in learning problems, (C2) the choice of appropriate spaces and metrics for measuring similarity, and (C3) the role of learning objectives beyond classification. For each challenge, we provide specific recommendations to help move the field forward, including formalizing temporal dynamics through distribution processes, developing principled approaches for continuous task spaces, and incorporating density estimation and generative objectives. In so doing, this position paper aims to broaden the scope of CL research while strengthening its theoretical foundations, making it more applicable to real-world problems.

Problem

• Expanding continual learning beyond incremental classification tasks.
• Addressing challenges in continuous task domains and higher-level concepts.
• Enhancing theoretical foundations and practical applicability of CL methods.

Innovation

• Expanding beyond incremental classification
• Formalizing temporal dynamics in learning
• Incorporating generative objectives